Točna 3D rekonstrukcija zasnovana na rotirajućoj platformi i telecentričnoj viziji

This paper presents a camera+telecentric lens that is able to obtain 3D information. We designed and implemented a method which can register and integrate 3D information captured from different viewpoints to build a complete 3D object model. First, a geometric model of a camera+telecentric lens is established. Then a calibration process using a planar checkerboard is developed and implemented. The object is placed on a rotation stage in front of a stationary camera. Normally the rotation axis is considered to be aligned with camera frame. In the description presented in this paper, the rotation matrix and translation vector of the rotation axis are calibrated. At the same time, a three-dimensional reconstruction system based on contour extraction of objects with dimensions less than 50 mm in diameter is developed. Finally, an analysis of the uncertainty model parameters and performance reconstruction of 3D objects are discussed.Članak prestavlja sustav koji se sastoji od kamere i telecentrične leće koji omogućavaju dobivanje 3D informacije o objektu. Dizajnirana je i implementirana metoda koja može registrirati i integrirati 3D informacije iz različitih točaka gledišta, kako bi se izgradio potpuni 3D model. Na početku, uspostavlja se geometrijski model kamere i telecentrične leće. Nakon toga koristi se razvijena metoda kalibracije zasnovana na šahovskoj ploči te se objekt postavlja na rotirajuću platformu ispred stacionarne kamere. Također, pretpostavlja se da je os rotacije poravnta s koordinantim sustavom kamere. U ovome članku kalibriraju se rotacijska matrica i translacijski vektor rotacijske osi. Razvijen je i sustav 3D rekonstrukcija zasnovan na izlučivanju kontura objekta dimenzija manjih od 50 mm u promjeru. Na kraju, provedena je i analiza nesigurnosti parametara modela kao i točnost rekonstrukcije 3D modela

Rational-operator-based depth-from-defocus approach to scene reconstruction

This paper presents a rational-operator-based approach to depth from defocus (DfD) for the reconstruction of three-dimensional scenes from two-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF) and the second based on the generalized Gaussian PSF, are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered for real scenes and show that both approaches outperform existing RO-based methods

A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses

We propose a novel multi-view camera model for line-scan cameras with telecentric lenses. The camera model supports an arbitrary number of cameras and assumes a linear relative motion with constant velocity between the cameras and the object. We distinguish two motion configurations. In the first configuration, all cameras move with independent motion vectors. In the second configuration, the cameras are mounted rigidly with respect to each other and therefore share a common motion vector. The camera model can model arbitrary lens distortions by supporting arbitrary positions of the line sensor with respect to the optical axis. We propose an algorithm to calibrate a multi-view telecentric line-scan camera setup. To facilitate a 3D reconstruction, we prove that an image pair acquired with two telecentric line-scan cameras can always be rectified to the epipolar standard configuration, in contrast to line-scan cameras with entocentric lenses, for which this is possible only under very restricted conditions. The rectification allows an arbitrary stereo algorithm to be used to calculate disparity images. We propose an efficient algorithm to compute 3D coordinates from these disparities. Experiments on real images show the validity of the proposed multi-view telecentric line-scan camera model

Calibration routine for a telecentric stereo vision system considering affine mirror ambiguity

A robust calibration approach for a telecentric stereo camera system for three-dimensional (3-D) surface measurements is presented, considering the effect of affine mirror ambiguity. By optimizing the parameters of a rigid body transformation between two marker planes and transforming the two-dimensional (2-D) data into one coordinate frame, a 3-D calibration object is obtained, avoiding high manufacturing costs. Based on the recent contributions in the literature, the calibration routine consists of an initial parameter estimation by affine reconstruction to provide good start values for a subsequent nonlinear stereo refinement based on a Levenberg–Marquardt optimization. To this end, the coordinates of the calibration target are reconstructed in 3-D using the Tomasi–Kanade factorization algorithm for affine cameras with Euclidean upgrade. The reconstructed result is not properly scaled and not unique due to affine ambiguity. In order to correct the erroneous scaling, the similarity transformation between one of the 2-D calibration plane points and the corresponding 3-D points is estimated. The resulting scaling factor is used to rescale the 3-D point data, which then allows in combination with the 2-D calibration plane data for a determination of the start values for the subsequent nonlinear stereo refinement. As the rigid body transformation between the 2-D calibration planes is also obtained, a possible affine mirror ambiguity in the affine reconstruction result can be robustly corrected. The calibration routine is validated by an experimental calibration and various plausibility tests. Due to the usage of a calibration object with metric information, the determined camera projection matrices allow for a triangulation of correctly scaled metric 3-D points without the need for an individual camera magnification determination

3D shape measurement of discontinuous specular objects based on advanced PMD with bi-telecentric lens

This paper presents an advanced phase measuring deflectometry (PMD) method based on a novel mathematical model to obtain three dimensional (3D) shape of discontinuous specular object using a bi-telecentric lens. The proposed method uses an LCD screen, a flat beam splitter, a camera with a bi-telecentric lens, and a translating stage. The LCD screen is used to display sinusoidal fringe patterns and can be moved by the stage to two different positions along the normal direction of a reference plane. The camera captures the deformed fringe patterns reflected by the measured specular surface. The splitter realizes the fringe patterns displaying and imaging from the same direction. Using the proposed advanced PMD method, the depth data can be directly calculated from absolute phase, instead of integrating gradient data. In order to calibrate the relative orientation of the LCD screen and the camera, an auxiliary plane mirror is used to reflect the pattern on the LCD screen three times. After the geometric calibration, 3D shape data of the measured specular objects are calculated from the phase differences between the reference plane and the reflected surface. The experimental results show that 3D shape of discontinuous specular object can be effectively and accurately measured from absolute phase data by the proposed advanced PMD method

A camera model for cameras with hypercentric lenses and some example applications

We propose a camera model for cameras with hypercentric lenses. Because of their geometry, hypercentric lenses allow to image the top and the sides of an object simultaneously. This makes them useful for certain inspections tasks, for which otherwise multiple images would have to be acquired and stitched together. After describing the projection geometry of hypercentric lenses, we derive a camera model for hypercentric lenses that is intuitive for the user. Furthermore, we describe how to determine the parameter values of the model by calibrating the camera with a planar calibration object. We also apply our camera model to two example applications: in the first application, we show how two cameras with hypercentric lenses can be used for dense 3D reconstruction. For an efficient reconstruction, the images are rectified such that corresponding points occur in the same image row. Standard rectification methods would result in perspective distortions in the images that would prevent stereo matching algorithms from robustly establishing correspondences. Therefore, we propose a new rectification method for objects that are approximately cylindrical in shape, which enables a robust and efficient reconstruction. In the second application, we show how to unwrap cylindrical objects to simplify further inspection tasks. For the unwrapping, the pose of the cylinder must be known. We show how to determine the pose of the cylinder based on a single camera image and based on two images of a stereo camera setup

Edge Detection and 3D Reconstruction Based on the Shape-from-Focus

Má práce vychází z průmyslového projektu, jehož cílem je postavit stroj pro přesnou manipulaci mikrokomponenty. Zmíněné mikrokomponenty jsou sledovány na základě hledání hran v obraze. Má práce popisuje přehled postupů používaných pro detekci hran v obraze a zároveň návrh algoritmu pro rekonstrukci povrchu mikrokomponent pomocí Shape-From-Focus v mikroskopickém prostředí. Použité obrázky byly pořízeny kamerou s telecentrickým objektivem s malou hloubkou ostrosti. Vyvinul jsem Shape-From-Focus algoritmus, který používá 3D konvoluční masku pro detekci hran a je schopný aproximovat povrchy bez struktury. Vyvinutá 3D konvoluční maska je založena na druhé derivaci obrazové funkce. V pokusech popisujících kalibraci kamery a pro opětovné zaostření optické soustavy byly použity rozličné metody pro detekci hran v obraze. V pokusech se také prezentují výsledky rekonstrukce povrchu pomocí navrženého Shape-From-Focus algoritmu.The work stems from the industrial project which aims to build the highly precise micro components assembly machine. The components are positioned via locating the edges in the image. The overview of the edge detection techniques and the design of the Shape-From-Focus algorithm in the microscopic environment are presented. The images used were captured with telecentric optics with a shallow Depth-of-Field. The Shape-From-Focus algorithm is developed together with the 3D convolutional mask and approximation of the surface in the textureless areas. The developed 3D convolutional filter is based on the seconds derivative of the image function. Various edge detection techniques are used in experiments to calibrate the camera and to refocus the optics. The experiments also show the surface reconstruction obtained by the Shape-From-Focus algorithm